Simulation is crucial in the development of autonomous driving software. In particular, assessing control algorithms requires an accurate vehicle dynamics simulation. However, recent publications use models with varying levels of detail. This disparity makes it difficult to compare individual control algorithms. Therefore, this paper aims to investigate the influence of the fidelity of vehicle dynamics modeling on the closed-loop behavior of trajectory-following controllers. For this purpose, we introduce a comprehensive Autoware-compatible vehicle model. By simplifying this, we derive models with varying fidelity. Evaluating over 550 simulation runs allows us to quantify each model's approximation quality compared to real-world data. Furthermore, we investigate whether the influence of model simplifications changes with varying margins to the acceleration limit of the vehicle. From this, we deduce to which degree a vehicle model can be simplified to evaluate control algorithms depending on the specific application. The real-world data used to validate the simulation environment originate from the Indy Autonomous Challenge race at the Autodromo Nazionale di Monza in June 2023. They show the fastest fully autonomous lap of TUM Autonomous Motorsport, with vehicle speeds reaching 267 kph and lateral accelerations of up to 15 mps2.

翻译：仿真在自动驾驶软件开发中至关重要。特别是评估控制算法需要精确的车辆动力学仿真。然而，近期发表的研究使用了不同详细程度的模型。这种差异使得比较各控制算法变得困难。因此，本文旨在研究车辆动力学建模的保真度对轨迹跟踪控制器闭环行为的影响。为此，我们引入了一个全面的兼容Autoware的车辆模型。通过简化该模型，我们推导出具有不同保真度的模型。通过评估超过550次仿真运行，我们能够量化每个模型相对于真实世界数据的近似质量。此外，我们研究了模型简化带来的影响是否会随着车辆加速度极限余量的变化而改变。基于此，我们推断出根据具体应用场景，车辆模型可以在何种程度上被简化以用于控制算法的评估。用于验证仿真环境的真实世界数据源自2023年6月在蒙扎国家赛车场举行的Indy Autonomous Challenge比赛。这些数据展示了TUM Autonomous Motorsport最快的一圈全自动驾驶记录，其中车速达到267公里/小时，横向加速度高达15米/秒²。